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from pathlib import Path
# == Embeddings model ==
EMBEDDING_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_MODEL_MAX_INPUT_LENGTH = 384
# == VECTOR Database ==
VECTOR_DB_OUTPUT_COLLECTION_NAME = "alpaca_financial_news2"
VECTOR_DB_SEARCH_TOPK = 1
# == LLM Model ==
LLM_MODEL_ID = "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"
LLM_QLORA_CHECKPOINT = "plantbased/mistral-7b-instruct-v0.2-4bit"
LLM_INFERNECE_MAX_NEW_TOKENS = 500
LLM_INFERENCE_TEMPERATURE = 1.0
# == Prompt Template ==
TEMPLATE_NAME = "mistral"
# === Misc ===
CACHE_DIR = Path.home() / ".cache" / "hands-on-llms"
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